CN110222452A - Oil-immersed transformer failure based on big data association mining deduces visualization system - Google Patents
Oil-immersed transformer failure based on big data association mining deduces visualization system Download PDFInfo
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- CN110222452A CN110222452A CN201910516004.8A CN201910516004A CN110222452A CN 110222452 A CN110222452 A CN 110222452A CN 201910516004 A CN201910516004 A CN 201910516004A CN 110222452 A CN110222452 A CN 110222452A
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- failure
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1281—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of liquids or gases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/041—Abduction
Abstract
The invention discloses a kind of, and the oil-immersed transformer failure based on big data association mining deduces visualization system, the two-way deduction of potential faults-failure symptom is carried out by typicalness amount fault type and failure symptom of the big data analysis digging technology to power transformer operating condition and association rule mining is retrieved, establish the diagnosing fault of power transformer model and case data retrieval management system of high efficiency smart, realize that the oil-immersed transformer failure based on big data association mining deduces visualization system using virtual reality technology, may to be that operating personnel comprehensively utilizes history fortune inspection information and carries out live fast failure/hidden danger association and deduces, auxiliary fortune inspection operating personnel carries out the fast and reliable analysis decision of Operation Condition of Power Transformers, the final intelligence and reliability level for improving Power Transformer Condition maintenance.
Description
Technical field
The present invention relates to power transformer technical field, in particular to a kind of oil immersed type based on big data association mining becomes
Depressor failure deduces visualization system.
Background technique
Power transformer is the important pivot equipment of electric system, is source-net-lotus reliable collaborative operation important composition portion
Point, Power Transformer Faults may result in serious electric system power outage, however, its all kinds of failure during operation
Problem is commonplace.For a long time, Utilities Electric Co. still uses the inspection at " based on periodic maintenance, supplemented by repair based on condition of component " to transformer
Repair strategy.As economic power system and the demand of safety are higher and higher, online electriferous state is carried out to power transformer and is overhauled
It is the inexorable trend that overhaul of the equipments mode develops, so improving transformer station high-voltage side bus maintenance and technology management level, reduces electric power and become
The generation of depressor failure is the core developing direction of electric power enterprise at this stage, while also to live O&M upkeep operation and member
More stringent requirements are proposed for work skills training.
Power transformer transimission power is big, structure is complicated, and different parts hidden danger/defect will lead to power transformer event
Barrier, and there is also larger differences for influence of the different running environment to power transformer working condition.It is past and when breaking down
It is past not only only to cause the variation of a failure symptom state, while also resulting in various faults sign and causing the mostly event of while property
Hinder type flaw, so that complicated intersection mapping association feature is presented between failure symptom and fault type, relies on expertise
Accumulation of knowledge is more difficult and versatility is poor, it is difficult to the event of transformer is solved by establishing power transformer expert system
Barrier diagnosis and forecasting problem, such O&M operating personnel bring great difficulty to the effective status assessment of power transformer.
Continuouss development that Power Transformer Faults deduce technology be based primarily upon diagnosing fault of power transformer and status assessment technology into
Electricity transformer monitoring big data is carried out effective analysis and utilization as great Yun object moves the development of technology by step, to realize that electric power becomes
The electriferous state maintenance of depressor provides effective technological means, can provide for power transformer or even the maintenance of substation's O&M
The more reliable aid decision of intelligence is supported.In recent years simultaneously, with the continuous improvement of smart grid and automatization level, electricity
Blowout situation is also gradually presented in the accumulation of power big data, and each Utilities Electric Co. all has accumulated the live transformer equipment operation of many years
A considerable amount of power transformer historical failure information and typical case under experience, including different running environment, different voltages grade
Example etc., the transformer fault deduction to be excavated based on big data provide data basis.
For these reasons, a kind of oil-immersed transformer failure deduction visualization system based on big data association mining is researched and developed
System is actually necessary.
Summary of the invention
The oil-immersed transformer failure based on big data association mining that the purpose of the present invention is to provide a kind of is deduced visual
Change system excavates means based on big data analysis and carries out Classification Management to efficient pressure swing device failure typical case, utilizes association
Regular method excavates composite transposition relationship between Power Transformer Faults sign and fault type, by establishing Power Transformer Faults
Diagnostic model and case data retrieval management system, deduce transformer fault, while the advantage of integrated virtual reality, build
Vertical effectively reliable Power Transformer Faults deduce visualization system, realize the visualization to transformer fault aid decision.
In order to achieve the above object, the invention is realized by the following technical scheme:
A kind of oil-immersed transformer failure based on big data association mining deduces visualization system, includes: power transformer
Device accident analysis case library includes the typical fault sign variable for reflecting transformer operating condition；Case data searching, managing module,
It carries out Classification Management to the typical fault sign variable in Power Transformer Faults analysis case library, and determines oil immersed type
Power Transformer Faults sign deduces table, to construct transformer typical fault model；Virtual reality visualization model, by institute
The data information combination virtual reality method in case data searching, managing module is stated, so that the transformer typical fault model
And the data information of setting is presented with threedimensional model and data visualization, the observation convenient for fortune inspection personnel to typical fault case
And research, Power Transformer Faults are quickly judged.
Preferably, the oil-immersed transformer failure based on big data association mining deduces visualization system, into one
Step includes: System right management module, for logging in after starting oil-immersed transformer failure deduces visualization system to user
Permission is judged.
Preferably, the oil-immersed power transformer failure symptom deduction table is divided into failure symptom mark, failure symptom class
Type, index parameter and operating status；The failure symptom mark and the failure symptom type are oil-immersed power transformer
It the default parameters of failure symptom deduction table and can not change；The index parameter shows the normal of corresponding failure symptom mark
The range of numerical value inserts currently detected real time data by clicking, to infer failure symptom；The operating status refers to
It is after the index parameter filling of corresponding failure symptom mark and whether normal according to the operating status of algorithm judgement.
Preferably, by all failure symptom Type divisions and corresponding 9 major break down diagnostic-types, respectively winding failure,
Iron core failure, insulation ag(e)ing, humidified insulation, insulating oil deterioration, current loop overheat, arc discharge, shelf depreciation and oil are stayed and are put
Electricity.
Preferably, each fault diagnosis type deduces button by clicking failure symptom, and failure symptom type is listed simultaneously
It is arranged according to weight.
The oil-immersed transformer event that the present invention also provides a kind of based on as described above based on big data association mining
The oil-immersed transformer failure that barrier deduces visualization system deduces method for visualizing, and this method includes following procedure: acquisition reflection
The typical fault sign variable of transformer operating condition；Classification Management is carried out to the typical fault sign variable, and determines oil immersed type
Power Transformer Faults sign deduces table, to construct transformer typical fault model；Make transformation by virtual reality method
Device typical fault model and the data information of setting are presented with threedimensional model and data visualization, convenient for fortune inspection personnel to typical case
The observation and research of fault case quickly judge Power Transformer Faults.
Preferably, the oil-immersed transformer failure deduces method for visualizing, further includes: becoming in starting oil immersed type
Depressor failure judges user's logon rights after deducing visualization system.
Preferably, the oil-immersed power transformer failure symptom deduction table is divided into failure symptom mark, failure symptom class
Type, index parameter and operating status；The failure symptom mark and the failure symptom type are oil-immersed power transformer
It the default parameters of failure symptom deduction table and can not change；The index parameter shows the normal of corresponding failure symptom mark
The range of numerical value inserts currently detected real time data by clicking, to infer failure symptom；The operating status refers to
It is after the index parameter filling of corresponding failure symptom mark and whether normal according to the operating status of algorithm judgement.
Preferably, by all failure symptom Type divisions and corresponding 9 major break down diagnostic-types, respectively winding failure,
Iron core failure, insulation ag(e)ing, humidified insulation, insulating oil deterioration, current loop overheat, arc discharge, shelf depreciation and oil are stayed and are put
Electricity.
Preferably, each fault diagnosis type deduces button by clicking failure symptom, and failure symptom type is listed simultaneously
It is arranged according to weight.
Compared with prior art, the invention has the benefit that
Oil-immersed transformer failure of the invention is deduced in Design of Visualization System, on the one hand, virtual reality technology is introduced,
Visual and clear fault type is provided by visualizing deduction mapping decisions result for live operation maintenance personnel and training employee
With the implicit associations of failure symptom；On the other hand, failure is shown with quantification manner by deducing mapping weight in intelligent algorithm
Sign to the implicit importance of fault type, be embodied as operating personnel selects O&M maintenance means, Power Transformer Faults are recalled,
It improves fault locating analysis efficiency and support is provided.
Detailed description of the invention
Fig. 1 is that the oil-immersed transformer failure deduction visualization system framework of the invention based on big data association mining shows
It is intended to；
Fig. 2 is that oil-immersed transformer failure of the invention deduces visualization system login interface schematic diagram；
Fig. 3 is fault type interface schematic diagram of the invention；
Fig. 4 is the failure symptom interface schematic diagram of the invention by taking winding failure as an example；
Fig. 5 is the fault case interface of the invention by taking winding short circuit as an example.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described
The oil-immersed transformer failure to be of the invention based on big data association mining deduces visualization system as shown in Figure 1
Configuration diagram.Oil-immersed transformer failure deduces visualization system: System right management module, Power Transformer Faults
Analyze case library, case data searching, managing module and virtual reality visualization model.
Wherein, it is logged in after oil-immersed transformer failure deduces visualization system in starting, system permission pipe
The permission that reason module is used to log in user judges, is illustrated in figure 2 oil-immersed transformer failure and deduces visualization system
Login interface schematic diagram.
It include anti-in existing Power Transformer Faults sample analysis report in Power Transformer Faults analysis case library
Reflect the typical fault sign variable of transformer operating condition.
Case data searching, managing module to existing Power Transformer Faults sample analysis report induction-arrangement on the basis of,
Typical fault sign progress Classification Management to transformer operating condition is reflected in the big data information pool of induction-arrangement building, determines oil
Immersion Power Transformer Faults sign deduces table, as shown in table 1.
As shown in table 1, oil-immersed power transformer failure symptom deduction table is divided into failure symptom mark, failure symptom class
Type, index parameter and operating status.
Wherein, failure symptom mark and failure symptom type are that the default parameters of table can not be changed.Index parameter is aobvious
The range for showing the regime values of corresponding failure symptom mark, can be inserted after clicking currently detected real time data to
Infer its failure symptom.According to algorithm judgement after the index parameter filling that operating status is identified for corresponding failure symptom
Whether operating status is normal.
In the present embodiment, by all failure symptom Type divisions and corresponding 9 major break down diagnostic-types, respectively winding event
Barrier, the deterioration of iron core failure, insulation ag(e)ing, humidified insulation, insulating oil, current loop overheat, arc discharge, shelf depreciation and oil stay
Electric discharge.Wherein, the feelings of the result prompt big type fault of current transformer 9 obtained according to all parameters that failure deduction table inputs
Condition, and unusual condition is marked out, as shown in Figure 3.
As shown in figure 4, by taking winding failure as an example, click failure symptom and deduce button, by failure symptom type list and according to
It is arranged according to weight, for example, insulating oil dielectric loss: 0.0817；Volume resistivity: 0.0886；Winding insulation dielectric loss: 0.1544；In oil
Air content: 0.0871；Furfural content: 0.2905；The value plate degree of polymerization: 0.2977.Meanwhile three-dimensional means also can be used and show one
A little fault cases, by taking winding short circuit as an example, as shown in Figure 5.
1 oil-immersed power transformer failure symptom of table deduces table
It is compound that the failure symptom of power transformer identifies the existing bidirectional crossed mapping of complexity between failure symptom type
It is connected to topological structure, is the basis of diagnosing fault of power transformer relationship maps model construction.Electric power change may be implemented in the present invention
Quick identification, building, retrieval and the extraction that mapping matrix and mapping are connected between depressor failure symptom and fault type.
Case data searching, managing module, will be in case data searching, managing module by virtual reality visualization model
Data information Combining with technology of virtual reality, by transformer typical fault model, indigestible data information with vivider intuitive
Threedimensional model and data visualization are presented, and the observation and research convenient for line fortune inspection personnel to typical fault case can more can
Easily grasp the quick judgement to Power Transformer Faults.
From the foregoing, it will be observed that the present invention is deduced by virtual reality fault visual, aid decision is believed in Virtual Reality Platform
Breath carries out intelligent presentation, realizes to apparent failure symptom associated by power transformer current failure and apparent failure symptom
Associated hidden failure type carries out the reinforcing cognition of quick bidirectional recognition and key decision tree fork, finally discriminates for operating personnel
O&M maintenance means are selected, realize Power Transformer Faults backtracking and the offer of fault locating analysis efficiency is provided is reliable theoretical
And technical support.
In conclusion the typicalness amount (event the present invention is based on big data analysis digging technology to reflection transformer operating condition
Hinder type and failure symptom) Classification Management is carried out, then excavated between fault type and failure symptom by association rules
Incidence relation is analyzed by the intelligent online to electric power big data fault case, realizes Power Transformer Faults sign and failure
Compound bidirectional crossed mapping between type, the diagnosing fault of power transformer model and case data for establishing high efficiency smart are retrieved
Management system, while the technical advantage of integrated virtual reality carries out visualizationization to decision-making assistant information and presents.
It is discussed in detail although the contents of the present invention have passed through above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read above content, for of the invention
A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (10)
1. a kind of oil-immersed transformer failure based on big data association mining deduce visualization system, characterized by comprising:
Power Transformer Faults analyze case library, include the typical fault sign variable for reflecting transformer operating condition；
Case data searching, managing module, to the typical fault sign variable in Power Transformer Faults analysis case library
Classification Management is carried out, and determines that oil-immersed power transformer failure symptom deduces table, to construct transformer typical fault model；
Virtual reality visualization model, by the data information combination virtual reality side in the case data searching, managing module
Method, so that the transformer typical fault model and the data information of setting are presented with threedimensional model and data visualization, just
Observation and research of the Yu Yunjian personnel to typical fault case, quickly judge Power Transformer Faults.
2. the oil-immersed transformer failure based on big data association mining deduces visualization system as described in claim 1,
It is characterized in that,
It further includes: System right management module, for right after starting oil-immersed transformer failure deduces visualization system
User's logon rights judge.
3. the oil-immersed transformer failure based on big data association mining deduces visualization system as claimed in claim 1 or 2,
It is characterized in that,
The oil-immersed power transformer failure symptom deduction table is divided into failure symptom mark, failure symptom type, index parameter
And operating status；
The failure symptom mark and the failure symptom type are the silent of oil-immersed power transformer failure symptom deduction table
Recognize parameter and can not change；
The index parameter shows the range of the regime values of corresponding failure symptom mark, by clicking filling current detection
The real time data arrived, to infer failure symptom；
The operating status refers to the operation judged after the index parameter filling of corresponding failure symptom mark and according to algorithm
Whether state is normal.
4. the oil-immersed transformer failure based on big data association mining deduces visualization system as claimed in claim 3,
It is characterized in that,
By all failure symptom Type divisions and corresponding 9 major break down diagnostic-types, respectively winding failure, iron core failure, absolutely
Edge aging, humidified insulation, insulating oil deterioration, current loop overheat, arc discharge, shelf depreciation and oil stay electric discharge.
5. the oil-immersed transformer failure based on big data association mining deduces visualization system as claimed in claim 4,
It is characterized in that,
Each fault diagnosis type deduces button by clicking failure symptom, and failure symptom type is listed and is arranged according to weight
Column.
6. a kind of oil-immersed transformer failure based on as claimed in claims 1-5 based on big data association mining is deduced visual
The oil-immersed transformer failure of change system deduces method for visualizing, which is characterized in that this method includes following procedure:
The typical fault sign variable of acquisition reflection transformer operating condition；
Classification Management is carried out to the typical fault sign variable, and determines that oil-immersed power transformer failure symptom deduces table,
To construct transformer typical fault model；
Make transformer typical fault model and the data information of setting with threedimensional model and data by virtual reality method
Visualization is presented, and Power Transformer Faults are quickly sentenced in the observation and research convenient for fortune inspection personnel to typical fault case
It is disconnected.
7. oil-immersed transformer failure as claimed in claim 6 deduces method for visualizing, which is characterized in that further include:
User's logon rights are judged after starting oil-immersed transformer failure deduces visualization system.
8. oil-immersed transformer failure as claimed in claims 6 or 7 deduces method for visualizing, which is characterized in that the oil immersion
Formula Power Transformer Faults sign deduction table is divided into failure symptom mark, failure symptom type, index parameter and operating status；
The failure symptom mark and the failure symptom type are the silent of oil-immersed power transformer failure symptom deduction table
Recognize parameter and can not change；
The index parameter shows the range of the regime values of corresponding failure symptom mark, by clicking filling current detection
The real time data arrived, to infer failure symptom；
The operating status refers to the operation judged after the index parameter filling of corresponding failure symptom mark and according to algorithm
Whether state is normal.
9. oil-immersed transformer failure as claimed in claim 8 deduces method for visualizing, which is characterized in that by all failures
Sign Type division simultaneously corresponding 9 major break down diagnostic-types, respectively winding failure, iron core failure, insulation ag(e)ing, humidified insulation,
Insulating oil deterioration, current loop overheat, arc discharge, shelf depreciation and oil stay electric discharge.
10. oil-immersed transformer failure as claimed in claim 9 deduces method for visualizing, which is characterized in that each failure is examined
Disconnected type deduces button by clicking failure symptom, and failure symptom type is listed and is arranged according to weight.
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| Application Number | Priority Date | Filing Date | Title |
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